Conventional methods for RNA 2D structure prediction search for minimal free energy structures. RNA's, however, RNA's do not always adopt global minimum structures. Rather, their structure is the result of the folding pathway followed by the structure in nature, which adopts sub-optimal folds occurring along the pathway. Our algorithm consists of an automaton that generates RNA structures by searching for optimal folding pathways. The automaton is endowed of operations to travel throughout the hyperspace of conformers embedded in a base pairing matrix. Using genetic programming it evolves optimizing its ability to find optimal pathways and finally 2D structures. Comparing the evolving automaton with conventional methods shows its potential.

Molecular flexibility change at bio macro-molecular complex formation is an important factor in molecular recognition and function analysis. We propose an index to compute flexibility loss/gain at complex formation/separation. The algorithm performs a rigidity and/or flexibility evaluation of every node (atom) on the network constituted by all the intra and inter-molecular inter-atomic interactions. Comparison of flexible or rigid molecular regions or domains within the complex with those in the respective isolated monomers leads to the index, quantifying the loss (or gain) in the number of degrees of freedom at complex formation. The index can also be applied to ranking decoys output by docking algorithms.

MOTIVATION: To find a correlation between the activities and structures of molecules is one of the most important subjects for molecular evaluation study. Traditional quantitative structure-activity relationship (QSAR) methodologies represent those attempts using physicochemical descriptors. Creating a new molecular description factor based on the results of a computational docking study will add new dimensions to molecular evaluation. RESULTS: We propose a new molecular description factor analysis system called the Comparative Molecular Interaction Profile Analysis (CoMIPA) system in which the AutoDock program is used for docking evaluation of small molecule compound-protein complexes. Interaction energies are calculated, and the data sets obtained are called interaction profiles (IPFs). Using the IPF as a scoring indicator, the system could be a powerful tool to cluster the interacting properties between small molecules and bio macromolecules such as ligand-receptor bindings. Further development of the system will enable us to predict the adverse effects of a drug candidate.